Inkling enters the arena — is an 'uncalibrated' model actually a feature?
mira murati's thinking machines lab launched inkling, a 975b parameter open-weight model that flags uncertainty instead of guessing. it targets enterprises tired of paying for proprietary models that absorb their data without yielding custom value.
thinking machines lab launched inkling, a 975 billion parameter open-weight model that activates roughly 41 billion parameters per task to maintain efficiency. trained on 45 trillion tokens of text, image, audio, and video, the model focuses on calibrated responses and native reasoning across modalities rather than raw benchmark dominance. the company pitches this as a direct alternative to proprietary 'black box' models, allowing enterprises to fully customize and own their ai stack. this move aligns with predictions from industry leaders like microsoft's satya nadella and hugging face's clem delangue, who anticipate a shift from general frontier models to specialized, company-tuned open alternatives.